Short-term Multi Horizons Forecasting of Solar Irradiation Based on Artificial Neural Network with Meteorological Data: Application in the North-west of Senegal - Laboratoire SYstèmes et Matériaux pour la MEcatronique Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Short-term Multi Horizons Forecasting of Solar Irradiation Based on Artificial Neural Network with Meteorological Data: Application in the North-west of Senegal

Résumé

This study proposes a short term forecasting of solar irradiation with multi horizons in the northwest of Senegal. The multilayer artificial neural network (ANN), based on the Levenberg Marquardt algorithm and the meteorological data are used. The latter are measured in real time on the study site. The variables of interest are: mean solar irradiation, maximum temperature and measurement time; they are selected using Weka software. The forecasting horizons are: 0.5 hour, 1 hour, 1.5 hours, 2 hours, 2.5 hours, 3 hours, 3.5 hours, 4 hours, 4.5 hours, 5 hours, 5.5 hours and 06 hours. They are proposed with the corresponding statistical criteria. The results show that, the solar energy forecasting can be extended over a six-hour horizon with a correlation coefficient of 0.97 and root mean square error of 0.07. These results will make it possible to complete the forecasting tools in the solar energy sector in Senegal, and help investors to choose the most suitable horizons for energy forecasting in photovoltaic solar power plants.
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Dates et versions

hal-03479555 , version 1 (14-12-2021)

Identifiants

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Willy Magloire Nkounga, Mouhamadou Falilou Ndiaye, Oumar Cisse, Momadou Bop, Françoise Grandvaux, et al.. Short-term Multi Horizons Forecasting of Solar Irradiation Based on Artificial Neural Network with Meteorological Data: Application in the North-west of Senegal. 2021 Sixteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), May 2021, Monte-Carlo, France. pp.1-8, ⟨10.1109/EVER52347.2021.9456600⟩. ⟨hal-03479555⟩
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